11/21/2023 0 Comments Subtitle edit memory overuseIn effect, the following diagram describes how nn.DataParallel works. The syntax remains similar to what we did earlier with nn.Module. We also need to make sure the DataParallel object is on that particular GPU as well. Despite the fact our data has to be parallelised over multiple GPUs, we have to initially store it on a single GPU. However, there are a few things I want to shed light over. an().backward() # Average GPU-losses + backward pass Loss = loss_function(predictions, labels) # Compute loss function predictions = parallel_net(inputs) # Forward pass on multi-GPUs Now, you can simply execute the nn.DataParallel object just like a nn.Module. parallel_net = nn.DataParallel(myNet, gpu_ids = ) You initialize a nn.DataParallel object with a nn.Module object representing your network, and a list of GPU IDs, across which the batches have to be parallelised. Model Parallelism, where we break the neural network into smaller sub networks and then execute these sub networks on different GPUs.ĭata Parallelism in PyTorch is achieved through the nn.DataParallel class.Data Parallelism, where we divide batches into smaller batches, and process these smaller batches in parallel on multiple GPU.There are two ways how we could make use of multiple GPUs. # Create a tensor of ones of size (3,4) on same device as of "ones"Ī detailed list of new_ functions can be found in PyTorch docs the link of which I have provided below. When a function like new_ones is called on a Tensor it returns a new tensor cof same data type, and on the same device as the tensor on which the new_ones function was invoked. One can also make use of the bunch of new_ functions that made their way to PyTorch in version 1.0. If operands are on different devices, it will lead to an error. If a tensor is created as a result of an operation between two operands which are on same device, so will be the resultant tensor. By default all tensors created by cuda call are put on GPU 0, but this can be changed by the following statement. We can also call cuda(n) while creating new Tensors. #making sure t2 is on the same device as t2 We can use this function to determine the device of the tensor, so that we can move a created tensor automatically to this device. It returns us the index of the GPU on which the tensor resides. ![]() In this regard, PyTorch provides us with some functionality to accomplish this.įirst, is the torch.get_device function. We want them to be automatically created on a certain device, so as to reduce cross device transfers which can slow our code down. While it's good to be able to explicitly decide on which GPU does a tensor go, generally, we create a lot of tensors during our operations. clf = myNetwork()Ĭlf.to(vice("cuda:0") # or clf = clf.cuda() Automatic selection of GPU Unlike, Tensors calling to on the nn.Module object is enough, and there's no need to assign the returned value from the to function. The torch.nn.Module class also has to adnd cuda functions which puts the entire network on a particular device. If you just call cuda, then the tensor is placed on GPU 0. cuda() functionĪnother way to put tensors on GPUs is to call cuda(n) function on them where n is the index of the GPU. Importantly, the above piece of code is device agnostic, that is, you don't have to separately change it for it to work on both GPU and the CPU. You can also move a tensor to a certain GPU by giving it's index as the argument to to function. You can check whether a GPU is available or not by invoking the _available function. Generally, whenever you initialise a Tensor, it's put on the CPU. Similarly, if you want to put the tensors on cuda:0 for putting it on GPU number 0.Input to the to function is a vice object which can initialised with either of the following inputs. It's job is to put the tensor on which it's called to a certain device whether it be the CPU or a certain GPU. You can get all the code in this post, (and other posts as well) in the Github repo here.Įvery Tensor in PyTorch has a to() member function. Memory Management and Using Multiple GPUs.Understanding Graphs, Automatic Differentiation and Autograd.How to diagnose and analyse memory issues should they arise.īefore we begin, let me remind you this part 4 of our PyTorch series.How to automate selection of GPU while creating a new objects. ![]()
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